We provide additional information about the experiments to supplement the main paper. The Gibson ObjectNav dataset from [1]} consists of 6 object categories: `chair', `couch', `potted plant', `bed', `toilet', and `tv'. The train split episodes are generated on-the-fly during training from 25 train scenes in Gibson tiny. The val split consists of \(1@000\)  episodes from 5 val scenes in Gibson tiny and is publicly accessible.Gibson ObjectNav dataset is accessible at https://github.com/devendrachaplot/Object-Goal-Navigation. The MP3D ObjectNav dataset from the Habitat challenge consists of 21 object categories: `chair', `table', `picture', `cabinet', `cushion', `sofa', `bed', `chest of drawers', `plant', `sink', `toilet', `stool', `towel', `tv monitor', `shower', `bathtub', `counter', `fireplace', `gym equipment', `seating', and `clothes'. The train / val splits consist of \(263@2422\)  / \(2@195\)  episodes from 61 / 11 MP3D scenes and are publicly released.MP3D ObjectNav dataset is accessible at https://github.com/facebookresearch/habitat-lab.
